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Frank Stinar; Zihan Xiong; Nigel Bosch – Journal of Educational Data Mining, 2024
Educational data mining has allowed for large improvements in educational outcomes and understanding of educational processes. However, there remains a constant tension between educational data mining advances and protecting student privacy while using educational datasets. Publicly available datasets have facilitated numerous research projects…
Descriptors: Foreign Countries, College Students, Secondary School Students, Data Collection
Zexuan Pan; Maria Cutumisu – AERA Online Paper Repository, 2023
Computational thinking (CT) is a fundamental ability for learners in today's society. Although CT assessments and interventions have been studied widely, little is known about CT predictions. This study predicted students' CT achievement in the ICILS 2018 using five machine learning models. These models were trained on the data from five European…
Descriptors: Computation, Thinking Skills, Artificial Intelligence, Prediction
José Luís Araújo; Isabel Saúde – Journal of Chemical Education, 2024
The rapid evolution of Artificial Intelligence (AI) is profoundly shaping our society. Among various AI tools, ChatGPT stands out for its user-friendly nature and wide accessibility to the public. However, despite their countless potential benefits, these tools also face significant challenges, especially in sensitive areas like Education. In this…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Chemistry
Quy, Tai Le; Roy, Arjun; Friege, Gunnar; Ntoutsi, Eirini – International Educational Data Mining Society, 2021
Traditionally, clustering algorithms focus on partitioning the data into groups of similar instances. The similarity objective, however, is not sufficient in applications where a "fair-representation" of the groups in terms of protected attributes like gender or race, is required for each cluster. Moreover, in many applications, to make…
Descriptors: Cluster Grouping, Artificial Intelligence, Mathematics, Computer Uses in Education
Costa-Mendes, Ricardo; Oliveira, Tiago; Castelli, Mauro; Cruz-Jesus, Frederico – Education and Information Technologies, 2021
This article uses an anonymous 2014-15 school year dataset from the Directorate-General for Statistics of Education and Science (DGEEC) of the Portuguese Ministry of Education as a means to carry out a predictive power comparison between the classic multilinear regression model and a chosen set of machine learning algorithms. A multilinear…
Descriptors: Foreign Countries, High School Students, Grades (Scholastic), Electronic Learning